scholarly journals Utility of polygenic embryo screening for disease depends on the selection strategy

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Todd Lencz ◽  
Daniel Backenroth ◽  
Einat Granot-Hershkovitz ◽  
Adam Green ◽  
Kyle Gettler ◽  
...  
Keyword(s):  
Complex Disease ◽  
Disease Risk ◽  
Threshold Model ◽  
Absolute Risk ◽  
Risk Scores ◽  
Selection Strategy ◽  
Case Control Studies ◽  
Disease Case ◽  
The Impact

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating ‘virtual’ couples and offspring based on real genomes from schizophrenia and Crohn’s disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

scholarly journals Utility of polygenic embryo screening for disease depends on the selection strategy

2020 ◽  
Author(s):  
Todd Lencz ◽  
Daniel Backenroth ◽  
Adam Green ◽  
Omer Weissbrod ◽  
Or Zuk ◽  
...  
Keyword(s):  
Absolute Risk ◽  
Disease Status ◽  
Risk Scores ◽  
Selection Strategy ◽  
Short Report ◽  
Minimal Risk ◽  
Ethical Concerns ◽  
Comprehensive Modeling ◽  
Variance Explained

AbstractAs of 2019, polygenic risk scores have been utilized to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. In this short report, we demonstrate that a strong determinant of the potential utility of such screening is the selection strategy employed, a factor that has not been previously studied. Minimal risk reduction is expected if only extremely high-scoring embryos are excluded, whereas risk reductions are substantially greater if the lowest-scoring embryo (for a given disease) is selected. We systematically examined the relative contributions of the variance explained by the score, the number of embryos, the disease prevalence, and parental scores and disease status on the utility of screening. We discuss the results in the context of relative vs absolute risk, as well as the potential ethical concerns raised by such procedures.

scholarly journals Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Qingsong Xi ◽  
Qiyu Yang ◽  
Meng Wang ◽  
Bo Huang ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Endometrial Thickness ◽  
Learning System ◽  
Embryo Selection ◽  
Selection Strategy ◽  
Application Study ◽  
Vitro Fertilization ◽  
The Impact

Abstract Background To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes. Results For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

scholarly journals Polygenic Hyperlipidemias and Coronary Artery Disease Risk

2019 ◽  
Author(s):  
Pietari Ripatti ◽  
Joel T Rämö ◽  
Nina J Mars ◽  
Sanni Söderlund ◽  
Christian Benner ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Cumulative Exposure ◽  
Risk Scores ◽  
Lipid Levels ◽  
Artery Disease ◽  
The Impact ◽  
Cad Risk

AbstractBackgroundHyperlipidemia is a highly heritable risk factor for coronary artery disease (CAD). Monogenic familial hypercholesterolemia associates with higher increase in CAD risk than expected from a single LDL-C measurement, likely due to lifelong cumulative exposure to high LDL-C. It remains unclear to what extent a high polygenic load of LDL-C or TG-increasing variants associates with increased CAD risk.Methods and ResultsWe derived polygenic risk scores (PRS) with ∼6M variants for LDL-C and TG with weights from a UK biobank-based genome-wide association study with ∼500K samples. We evaluated the impact of polygenic hypercholesterolemia and hypertriglyceridemia to lipid levels in 27 039 individuals from the FINRISK cohort, and to CAD risk in 135 300 individuals (13 695 CAD cases) from the FinnGen project.In FINRISK, LDL-C ranged from 2.83 (95% CI 2.79-2.89) to 3.80 (3.72-3.88) and TG from 0.99 (0.95-1.01) to 1.52 (1.48-1.58) mmol/l between the lowest and highest 5% of the respective PRS distributions. The corresponding CAD prevalences ranged from 8.2% to 12.7% for the LDL-C PRS and from 8.2% to 12.1% for the TG PRS in FinnGen. Furthermore, CAD risk was 1.36-fold higher (OR, 95% CI 1.24-1.49) for the LDL-C PRS and 1.31-fold higher (1.20-1.44) for the TG PRS for those with the PRS >95th percentile vs those without. These estimates were only slightly attenuated when adjusting for a CAD PRS (OR 1.26 [95% CI 1.15-1.39] for LDL-C and 1.21 [1.10-1.32] for TG PRS).ConclusionsThe CAD risk associated with a high polygenic load for lipid-increasing variants was proportional to their impact on lipid levels and mostly independent of a CAD PRS. In contrast with a PRS for CAD, the lipid PRSs point to known and directly modifiable risk factors providing more direct guidance for clinical translation.

scholarly journals Genetic Polymorphisms Associated with Rheumatoid Arthritis Development and Antirheumatic Therapy Response

2020 ◽  
Vol 21 (14) ◽  
pp. 4911 ◽  
Author(s):  
Dmitry S. Mikhaylenko ◽  
Marina V. Nemtsova ◽  
Irina V. Bure ◽  
Ekaterina B. Kuznetsova ◽  
Ekaterina A. Alekseeva ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Candidate Gene ◽  
Genetic Variants ◽  
Disease Risk ◽  
Significant Proportion ◽  
Therapy Response ◽  
Risk Groups ◽  
Antirheumatic Therapy ◽  
Disease Case ◽  
The Impact

Rheumatoid arthritis (RA) is the most common inflammatory arthropathy worldwide. Possible manifestations of RA can be represented by a wide variability of symptoms, clinical forms, and course options. This multifactorial disease is triggered by a genetic predisposition and environmental factors. Both clinical and genealogical studies have demonstrated disease case accumulation in families. Revealing the impact of candidate gene missense variants on the disease course elucidates understanding of RA molecular pathogenesis. A multivariate genomewide association study (GWAS) based analysis identified the genes and signalling pathways involved in the pathogenesis of the disease. However, these identified RA candidate gene variants only explain 30% of familial disease cases. The genetic causes for a significant proportion of familial RA have not been determined until now. Therefore, it is important to identify RA risk groups in different populations, as well as the possible prognostic value of some genetic variants for disease development, progression, and treatment. Our review has two purposes. First, to summarise the data on RA candidate genes and the increased disease risk associated with these alleles in various populations. Second, to describe how the genetic variants can be used in the selection of drugs for the treatment of RA.

scholarly journals Orlistat and cardiovascular risk profile in hypertensive patients with metabolic syndrome: the ARCOS study

2006 ◽  
Vol 50 (2) ◽  
pp. 368-376 ◽  
Author(s):  
Maria Teresa Zanella ◽  
Marcelo Hiroshi Uehara ◽  
Artur Beltrame Ribeiro ◽  
Marcelo Bertolami ◽  
Ana Claudia Falsetti ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Weight Loss ◽  
Cardiovascular Risk ◽  
Glucose Metabolism ◽  
Disease Risk ◽  
Risk Scores ◽  
Risk Category ◽  
Coronary Risk ◽  
Hypertensive Patients ◽  
The Impact

Weight loss improves metabolic abnormalities and reduces cardiovascular risk in obese hypertensive patients. To evaluate the impact of a sustained weight loss on coronary risk, 181 hypertensive patients with metabolic syndrome underwent to orlistat therapy, 120 mg, t.i.d., plus diet for 36 weeks. During therapy, Framingham risk scores (FRS) were calculated for determination of coronary heart disease risk in ten years. Body mass index decreased from 35.0 ± 4.2 to 32.6 ± 4.5 kg/m² (p< 0.0001) and waist circumference from 108.1 ± 10.1 to 100.5 ± 11.1 cm (p< 0.0001), at the end of the study period (week 36). Systolic and diastolic blood pressure showed reductions after the two first weeks, which were maintained up to the end of the study. A clear shift to the left in FRS distribution curve occurred at the end of the study, compared to baseline, indicating a reduction in coronary risk. Over all patients at risk, 49.2% moved to a lower risk category. A weight loss > 5% occurred in 64.6% of all patients, associated with improvement in glucose metabolism. Among those with abnormal glucose metabolism, 38 out 53 patients (71.7%) improved their glucose tolerance (p< 0.0005). In conclusion, long-term orlistat therapy helps to reduce and maintain a lower body weight, decreasing risk of coronary disease and improving glucose metabolism, thus protecting against type 2 diabetes.

scholarly journals Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiangming Sun ◽  
Yunpeng Wang ◽  
Lasse Folkersen ◽  
Yan Borné ◽  
Inge Amlien ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Complex Disease ◽  
Disease Risk ◽  
Disease Status ◽  
Risk Scores ◽  
Correct Prediction ◽  
Confidence Bounds ◽  
Conformal Prediction ◽  
Polygenic Risk ◽  
Individual Level

AbstractA promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual’s disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.

scholarly journals Polygenic Hyperlipidemias and Coronary Artery Disease Risk

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Pietari Ripatti ◽  
Joel T. Rämö ◽  
Nina J. Mars ◽  
Yu Fu ◽  
Jake Lin ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Odds Ratio ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Risk Scores ◽  
Lipid Levels ◽  
Artery Disease ◽  
The Impact ◽  
Cad Risk

Background: Hyperlipidemia is a highly heritable risk factor for coronary artery disease (CAD). While monogenic familial hypercholesterolemia associates with severely increased CAD risk, it remains less clear to what extent a high polygenic load of a large number of LDL (low-density lipoprotein) cholesterol (LDL-C) or triglyceride (TG)-increasing variants associates with increased CAD risk. Methods: We derived polygenic risk scores (PRSs) with ≈6M variants separately for LDL-C and TG with weights from a UK Biobank–based genome-wide association study with ≈324K samples. We evaluated the impact of polygenic hypercholesterolemia and hypertriglyceridemia to lipid levels in 27 039 individuals from the National FINRISK Study (FINRISK) cohort and to CAD risk in 135 638 individuals (13 753 CAD cases) from the FinnGen project (FinnGen). Results: In FINRISK, median LDL-C was 3.39 (95% CI, 3.38–3.40) mmol/L, and it ranged from 2.87 (95% CI, 2.82–2.94) to 3.78 (95% CI, 3.71–3.83) mmol/L between the lowest and highest 5% of the LDL-C PRS distribution. Median TG was 1.19 (95% CI, 1.18–1.20) mmol/L, ranging from 0.97 (95% CI, 0.94–1.00) to 1.55 (95% CI, 1.48–1.61) mmol/L with the TG PRS. In FinnGen, comparing the highest 5% of the PRS to the lowest 95%, CAD odds ratio was 1.36 (95% CI, 1.24–1.49) for the LDL-C PRS and 1.31 (95% CI, 1.19–1.43) for the TG PRS. These estimates were only slightly attenuated when adjusting for a CAD PRS (odds ratio, 1.26 [95% CI, 1.16–1.38] for LDL-C and 1.24 [95% CI, 1.13–1.36] for TG PRS). Conclusions: The CAD risk associated with a high polygenic load for lipid-increasing variants was proportional to their impact on lipid levels and partially overlapping with a CAD PRS. In contrast with a PRS for CAD, the lipid PRSs point to known and directly modifiable risk factors providing additional guidance for clinical translation.

Paternal Diet and Obesity: Effects on Reproduction

2017 ◽  
Vol 35 (04) ◽  
pp. 313-317 ◽  
Author(s):  
Alex Polotsky ◽  
Jasmine Aly
Keyword(s):  
Maternal Obesity ◽  
Absolute Risk ◽  
Human Studies ◽  
Semen Parameters ◽  
Sperm Dna Fragmentation ◽  
Vitro Fertilization ◽  
Sperm Dna Damage ◽  
Sperm Dna ◽  
The Impact

AbstractAlthough most research has focused on maternal obesity, there is growing data to indicate that obesity in the father can affect reproduction. Supporting data come from both mouse and human studies. Murine studies found that obese male mice exhibited decreased motility and reduced hyperactivated progression versus lean mice. Obese mice also exhibited sperm with increased levels of intracellular and mitochondrial levels of reactive oxygen species, increased sperm damage, and lower levels of capacitation, which has been shown to be associated with poor fertilization rates following in vitro fertilization, defective preimplantation embryonic development, and high rates of miscarriage and morbidity in the offspring. Furthermore, diet-induced paternal obesity was found to initiate intergenerational transmission of obesity and insulin resistance in two generations of murine offspring. Meta-analysis from human studies found obese males were more likely to demonstrate sperm DNA fragmentation, infertility, decreased live birth per cycle of assisted reproduction technology, and increased absolute risk of pregnancy nonviability, with no consistent effect on conventional semen parameters. There is a need for future studies to expound on the mechanisms of sperm DNA damage and the impact of weight loss in reversing this damage.

scholarly journals Individualized Embryo Selection Strategy Developed by Stacking Machine Learning Model for Better In Vitro Fertilization Outcomes: An Application Study.

2021 ◽  
Author(s):  
Qingsong XI ◽  
Qiyu YANG ◽  
Meng WANG ◽  
Bo HUANG ◽  
Bo ZHANG ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Endometrial Thickness ◽  
Learning System ◽  
Embryo Selection ◽  
Selection Strategy ◽  
Application Study ◽  
Vitro Fertilization ◽  
The Impact

Abstract Background: To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods: This was an application study including 7887 patients with 8585 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.Results: For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1+P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1×P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion: Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

scholarly journals Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories

2020 ◽  
Author(s):  
Ricky Lali ◽  
Michael Chong ◽  
Arghavan Omidi ◽  
Pedrum Mohammadi-Shemirani ◽  
Ann Le ◽  
...  
Keyword(s):  
Rare Variant ◽  
Genetic Risk ◽  
Complex Disease ◽  
Rare Variants ◽  
Disease Risk ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Considerable Proportion ◽  
Asian Populations ◽  
Artery Disease

ABSTRACTRare variants are collectively numerous and may underlie a considerable proportion of complex disease risk. However, identifying genuine rare variant associations is challenging due to small effect sizes, presence of technical artefacts, and heterogeneity in population structure. We hypothesized that rare variant burden over a large number of genes can be combined into predictive rare variant genetic risk score (RVGRS). We propose a novel method (RV-EXCALIBER) that leverages summary-level data from a large public exome sequencing database (gnomAD) as controls and robustly calibrates rare variant burden to account for the aforementioned biases. A RVGRS was found to strongly associate with coronary artery disease (CAD) in European and South Asian populations. Calibrated RVGRS capture the aggregate effect of rare variants through a polygenic model of inheritance, identifies 1.5% of the population with substantial risk of early CAD, and confers risk even when adjusting for known Mendelian CAD genes, clinical risk factors, and common variant gene scores.

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